1. Technical Field
The present invention relates to risk/cost analysis tools for estimating future health care costs, and in particular to tools for retrospective review and profiling to create risk groups.
2. Description of the Related Art
Estimates of the anticipated health care requirements and costs for a group of patients may be used for a variety of purposes, most notably anticipating costs for insurance and other purposes related to the financing of health care. Estimates typically are made by analyzing the historical records of the members of the population for which the estimate is being made and extrapolating future health care requirements from clinical and other defined characteristics of the population. Systems for doing this are generally referred to as risk adjusters, since they categorize individuals based on their future risk for needing health care services. However, the risk adjusters to date have operated only at a high level, with the result that their efficacy and utility is limited.
In early risk adjusters, weights were calculated for each of a set of diagnostic categories and/or cost groups using a linear regression model. Only a single category, the most expensive, is chosen to estimate an individual's future costs and all other diagnoses are ignored. While a single factor clearly is inadequate for individuals with multiple problems, an additive approach is occasionally used. Currently available products, such as Hierarchical Coexisting conditions (HCC), determine the weight given each disease group using a linear regression model which assigns a weight for each of a set of diagnostic categories. Then, where and when applicable, the weights for each diagnostic category in an individual's history are added to get a total weight. The total weight is converted into a predicted cost for the next year. Additive approaches, however, may also not accurately represent the relationship between ostensibly independent problems. For example, consider the case of individuals with diabetes and hypertension, which generally are considered independent but interactive disease processes. While diabetes does not cause hypertension, or vice versa, it is not unusual for an individual to have both. However, the additional or marginal cost for treating a diabetic with hypertension may actually be considerably less than simply adding the cost for treating a non-hypertensive diabetic to the cost for treating a non-diabetic hypertensive. This makes intuitive sense when one considers that the diabetic already is making regular office visits for the diabetes, blood pressure is routinely checked during any medical office visit, so the costs are likely not equal to the costs of treating diabetes and hypertension independently.
Some past risk adjustment systems (e.g., the Medicare Diagnosis Related Groups, or DRGs) include some historical indication of overall severity at a particular time in a particular setting, but they do not explicitly identify severity by category of disease or project its likely impact upon medical needs into the future. The severity level of a disease can directly affect how that disease interacts with other diseases, and the consequent need for future care. To continue with the example just described, the example probably is true for low severity diabetes and low severity hypertension, but the opposite may be true for high severity diabetes and high severity hypertension. When both diseases are high severity, they can interact, making both diseases harder to treat. A system not explicitly incorporating severity into its logic will not identify this interaction risk.
Estimating costs using weights reflecting individuals from different points in the disease process also can be misleading. Individual health care needs vary not only by disease, but also by severity of disease. For example, at least in its early stages, hypertension is a relatively minor condition for many people, controllable by diet and exercise. However, for people in more advanced stages, it may be a fairly serious problem. It may require aggressive treatment, including occasional hospitalization, as well as posing a high risk of other significant cardiovascular problems. A single weight will not accurately reflect the severity of a disease experienced by individuals at different stages of the disease. No current risk adjustment system explicitly identifies levels of severity of disease.
Current systems also ignore the temporal aspects of care, such as treatment which may eliminate prior problems. For example, a patient with angina who undergoes a coronary arterial bypass graft (CABG) would not be expected to experience a recurrence of angina in the period following the bypass. But current systems do not take this into account—if angina has been recorded at any time, they continue to assume angina is present until such time as it no longer appears in the data.
The current systems, such as that in U.S. Pat. No. 5,557,514, were designed to predict future costs to allow calculation of insurance rates and identification of providers with high utilization profiles, but are of limited value to in helping providers to actually control costs. The capitated payment arrangements typical of health maintenance organizations and preferred provider organizations place the majority of financial risk on the providers of care. The underlying assumption is that since providers are responsible for the delivery of care, they can respond to the incentives to control costs inherent in a capitated payment system.
The success of any payment system that is predicated on providing incentives for cost control is almost totally dependent on the effectiveness with which the incentives are communicated to providers. Payers need to express the payment arrangements in a form that communicates the incentives in the system in a manner and at a level of detail that promotes effective management responses.
But detailed clinical descriptions are not considered in current systems, and, more importantly, explicit severity levels and interactions among clinical conditions are not a part of a group assignment. Therefore, data from such systems is of limited value to clinicians, who need to understand the clinical basis of their costs in order to respond effectively to incentives inherent in capitated payment systems. While it sometimes is possible to use the information from such risks adjuster to identify where pro-active efforts could substantially reduce problems (and costs), it is very difficult.